Author
Affiliation

Caspar David Peter

Rotterdam School of Management, Accounting Department

Data Analytics for Finance

BM17FI · Rotterdam School of Management

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COURSE PORTAL

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Course Overview

Welcome to the Data Analytics for Finance course materials. This website contains all the lecture slides, assignments, and resources for the course.

Course outline

This course is designed to equip students with comprehensive data analysis skills using Stata and a thorough understanding of research methods in finance. The course emphasizes both theoretical foundations and practical implementation of econometric techniques essential for empirical finance research.

Students will learn how to apply statistical analysis techniques with proper understanding of underlying assumptions, interpret data effectively, and address common methodological challenges in financial research. These skills directly support students’ ability to successfully complete their master thesis replication projects and prepare them for data-intensive roles in the financial industry.

Learning goals

By the end of this course, students will be able to:

  1. Data Management: Clean, transform, and merge complex datasets using different identifiers (CUSIP, GVKEY, PERMNO) in Stata
  2. Econometric Theory: Understand and verify the assumptions underlying statistical methods used in finance research
  3. Panel Methods: Implement and interpret fixed effects models with appropriate standard error corrections
  4. Causal Inference: Apply difference-in-differences and event study methodologies to identify causal effects
  5. Research Communication: Create publication-quality tables and visualizations for effective presentation of empirical findings
  6. Replication Skills: Execute replications of published research with proper documentation and Stata code

Schedule

The course consists of 6 sessions, each lasting 2 hours 45 minutes. The sessions will be held in person on campus. The course includes individual assignments that reinforce the concepts learned in class.

Lectures

  1. From Messy Data to Research Insights
  2. Data Exploration and Visualization
  3. Regressions and Identification
  4. De Do Do Do, De Di i Di - Panel Data Methods
  5. Event Studies (CAR) and Fama–MacBeth
  6. Replication Workshop

Materials

Canvas

Canvas will be used for announcements and sharing of resources.

Website

All lecture slides, and additional resources are available on this website. Students are encouraged to review the materials before and after each session to reinforce their understanding.

Assignments

Assignments will be available via Jupyter Hub. You will receive a personal invitation to access the platform via Canvas.

Assessment

Individual assignments

  • Six (weekly) assignments throughout the course
  • All assignments must be completed

Assignment Overview:

  1. Dieselgate - Data Wrangling
  2. Dieselgate - Data Visualization
  3. Dieselgate - Regression Analysis
  4. Dieselgate - Difference-in-Differences
  5. Dieselgate - Event Study
  6. Replication Exercise - Replicating a Published Study with a Twist

Pass Requirements:

  • Overall course grade ≥ 5.5
  • Assignment grades ≥ 50%

Software and Tools

Students will use:

  1. Stata (version 17 or higher) - All econometric analysis
  2. Data from financial databases available through university subscriptions

All software is available through university resources. Basic familiarity with statistics is expected.

Resources

Required Textbooks

  1. Huntington-Klein, N. (2022). The Effect: An Introduction to Research Design and Causality (2nd ed.). Chapman and Hall/CRC.
    • Free online version available at theeffectbook.net
    • Required reading for causal inference concepts
    • Specific chapters assigned for each session

Additional resources, recommended readings, and useful links will be provided throughout the course.